Advisory.
Your AI that guides.
Forecasts, scenarios, recommended next moves.
Forecasts, scenarios, and prioritized recommendations with ROI quantified. Guidance that anticipates what comes next, and tells you what to do about it.
The Advisory Engine projects KPIs forward, simulates what-if scenarios, surfaces risks early, and converts every recommendation into an executable plan the Actions Engine can run.
What Advisory does for you
Four core jobs, done well.
Forward-looking forecasts
KPIs, cashflow, demand, and churn projected forward with calibrated confidence bands, not point estimates.
Scenario planning
What-if simulations across decision paths. See downstream impact before you commit.
Prioritized recommendations
Not every possibility. A ranked shortlist with ROI, effort, and risk attached.
Executable action plans
Every recommendation converts to a concrete plan the Actions Engine can run.
What's in the box
The Advisory Engine, and the Foundation underneath.
Every Quadrazene Engine ships with the same Foundation. Advisory adds its own Skills, atoms, and Reactions on top.
The Advisory Engine adds
Advisory Skills library
At-risk customers, deal prioritization, retention playbook, supplier-spend, journal-entry review, demand forecasts.
Advisory-recommender
Synthesizes Insights + Governance findings into ranked recommendations tied to executable Actions.
ML inference pipeline
Gradient-boosted quantile regression (p50/p90), logistic classifier, model-trainer. All explainable with SHAP-style waterfalls.
Promote to Action Item
One click captures category, priority, owner, due date, and source provenance into the Inbox.
Forecasting + confidence bands
Forecasts ship with bands so risk is sized, not implied.
Scheduled retrain Chains
ML models retrain on cron. Weights versioned in the Model registry, reproducible from provenance.
Foundation · included
- ›Reactor - The chat workspace where every question lands.
- ›Compose - Describe what you need. An agent drafts the Skill, Formula, or View.
- ›Designer - Dual-pane visual + NL editor for Formulas and Views.
- ›Recipes - Formulas chain into Chains. Versioned, replayable runs.
- ›Skills - Parameterized prompt templates with {{template}} variables.
- ›Inbox - HITL approvals, Action Items, and Findings in one place.
- ›Mailbox - Email + channel intake. Parse, classify, route.
- ›Records - Immutable provenance timeline for every Reaction.
- ›Connections - Data sources, SAP, MCP servers, Teams, and the API spine.
- ›Trust Layer - Content filters, prompt-injection detection, model allowlist, classification cap.
- ›Risk - Composite 0-100 score per Reaction. Auto-HITL above threshold.
- ›Models - Multi-provider LLM routing with a spend dashboard.
Every Engine ships with the full Foundation. No separate purchase.
Live sample · real data from the app database
Not a mockup. The platform actually produced this.
Run the at-risk classifier for project 662449. Show P(slip), the predicted slip-weeks for context, and the strongest positive drivers from the contribution breakdown.
| project number | customer | division | status | total tons | recent change count | late stage change pct | weeks since last change | milestone overdue pct | critical overdue count | division id | predicted slip weeks p50 | predicted slip weeks p90 | probability at risk |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 662449 | McCarthy Building | Bridge & Highway | ACTIVE | 295 | 7 | 0.143 | 5.286 | 0 | 0 | 2 | 0.19 | 0.19 | 0.412 |
The project 662449, under McCarthy Building in the Bridge & Highway division, has a notable probability at risk of 41.2%, indicating a significant chance of project delays. The predicted slip weeks are minimal at 0.19 for both P50 and P90, suggesting that while the risk is present, the expected delay is relatively low. The primary driver for this risk is the weeks since the last change, which stands at 5.29 weeks, hinting at potential stagnation in project progress.
- The probability at risk of 41.2% indicates a substantial likelihood of project delays, which is noteworthy for an active project.
- Despite the risk, the predicted slip weeks are only 0.19, suggesting that any delays may be minor in magnitude.
- The weeks since the last change at 5.29 weeks is the top driver for both the regression and classification models, highlighting a potential area of concern for project momentum.
Charts, narrative, findings, and payloads are exactly what the platform produced. Sanitized for display.
Bond more Engines
Each Engine bonded into the Ring catalyzes the others.
Advisory stands on its own. Bond another Engine and the loop closes. Here's what each pairing unlocks.
Recommendations grounded in real data.
Every Advisory suggestion cites the Insights queries it's built on. The recommender never proposes a move without the rows that justify it.
Add Insights →Recommendations shaped by your policies.
Governance findings roll into the recommender's prioritization. High-severity policy hits surface as “fix this first.”
Add Governance →Recommendations that execute.
Promote any Advisory suggestion to an Action Item the Actions Engine runs. The plan ships with the ERP/ITSM/email payload already drafted.
Add Actions →Where it pays off
Advisory in the real world.
CFO / Finance
Rolling forecasts, cashflow scenarios, capital allocation, with assumptions visible and editable.
Sales / RevOps
Pipeline quality signals, churn prediction, deal prioritization, territory planning.
Supply chain
Demand forecasting, inventory optimization, vendor-risk scoring, lead-time prediction.
Sample questions
What users actually ask.
Use it however you want
The Reactor, or your own framework.
Use Advisory as a decision-intelligence tool inside your existing platform. Your Agent Core can call advisory-recommender or any ML Skill over REST. Receive the ranked recommendations and execute them in whichever runtime you already operate.
Two adoption patterns
Put the Advisory Engine to work.
A working session with your own data. Start with Advisory. Bond more Engines when you're ready.